Malware behavioural detection and vaccine development by using a support vector model classifier

Most existing approaches for detecting viruses involve signature-based analyses to match the precise patterns of malware threats. However, the problem of classification accuracy regarding unspecified malware detection depends on correct extraction and completeness of training signatures. In practice, malware detection system uses the generalization ability of support vector models (SVMs) to guarantee a small classification error by machine learning. This study developed an automatic malware detection system by training an SVM classifier based on behavioural signatures. A cross-validation scheme was used for solving classification accuracy problems by using SVMs associated with 60 families of real malware. The experimental results reveal that the classification error decreases as the sizing of testing data is increased. For different sizing (N) of malware samples, the prediction accuracy of malware detection goes up to 98.7% with N = 100 . The overall detection accuracy of the SVC is more than 85% for unspecific mobile malware.